Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks

An industrial image classification case study was utilized to compare PLS-DA, RF, and DNN models. Compared to the in situ classification system currently in use, increasingly complex models (PLS-DA and RF) were able to better utilize the same pre-defined features and improve prediction accuracy significantly. DNN obtained the highest accuracy on the independent test set, with the advantages of not requiring the a priori computation of image features since they are directly extracted from the raw images. Moreover, by visualizing the output of some layers of the DNN, it is possible to verify that activations occurred in regions that are indeed meaningful for the classification tasks, further supporting that DNN were effectively modelling the relevant features of the pellet.